Proposal of a CNN-based Method for Predicting the Number of Clicks on Micro-event Flyer Images

Abstract

The web service “Tame-Map” enables users to easily transmit, view, and share information about small local activities, known as micro-events, which typically receive less attention on mainstream social media platforms. This study aims to develop a method for predicting the click rates of micro-event announcement images on “Tame-Map” and to evaluate the effectiveness of this approach. The research primarily utilizes machine learning techniques and constructs a predictive model employing Convolutional Neural Networks (CNN). The model assumes that thumbnail images of micro-events possess distinct features that attract viewers and utilizes these images as primary input data. Moreover, the study introduces an advanced model that incorporates meta-information, such as the event's location, as additional explanatory variables. The resulting model demonstrates adequate predictive accuracy, suggesting its potential as a standard method in future applications.